# set groupings of colors as palettes
dt_palettes <- list(
`main` = get_dt_cols("teal", "darkteal", "honeymustard"),
`diverging` = get_dt_cols("darkteal", "lavender", "sand", "honeymustard"),
`cool` = get_dt_cols("darkteal", "teal", "seafoam", "lavender"),
`desert` = get_dt_cols("sand", "honeymustard", "rust", "cocoa"),
`mixed` = get_dt_cols("cocoa", "rust","honeymustard", "sand", "lavender", "seafoam", "teal", "darkteal"),
`grey` = get_dt_cols("palegray", "darkgray")
)
theme_modest <- function(base_size = 12,
base_family = "Roboto",
base_line_size = base_size / 25,
base_rect_size = base_size / 25) {
require(grid)
theme_minimal(base_size = base_size,
base_family = base_family,
base_line_size = base_line_size) %+replace%
theme(
axis.title = element_text(size = rel(0.85)),
axis.text = element_text(size = rel(0.6)),
axis.ticks=element_line(colour="grey", size=0.5),
panel.grid.major = element_line(colour="grey", size=0.4),
panel.grid.minor = element_line(colour="lightgrey", size=0.25),
legend.box = NULL,
legend.text = element_text(size = rel(0.75)),
legend.key.height = NULL,
legend.key.width = NULL,
legend.key = element_rect(fill = NA, colour = NA),
legend.background = element_rect(colour = NA, fill=NA),
legend.position = "right",
legend.justification = "center",
plot.background = element_rect(colour = NA, fill=NA),
plot.margin = unit(c(0,0,0,0),"lines"),
plot.title = element_text(size = rel(1.5), face = "bold", hjust="0.5", margin = margin(t = 15)),
plot.subtitle = element_text(size = rel(1.25), face = "bold", hjust="0.5", margin = margin(t = 15)),
plot.caption = element_text(size = rel(0.75), hjust = 1),
strip.background = element_rect(colour = NA, fill=NA),
strip.text = element_text(face = "bold", size = rel(1.25)),
panel.spacing = unit(0,"lines"),
panel.border=element_blank(),
complete = TRUE
)
}
theme_map_modest <- function(base_size = 12) {
require(grid)
theme_modest(base_size) %+replace%
theme(
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text = element_blank(),
axis.ticks=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.major.x=element_blank(),
panel.border=element_blank(),
panel.spacing = unit(0,"lines"),
plot.margin = unit(c(2,2,2,2),"lines")
)
}
theme_set(theme_modest(base_size = 12))
# bus_licenses <- read_csv(here::here("data", "Business_Licenses.csv"))
bus_licenses <- readRDS(here::here("data", "bus_licenses.Rda"))
# all_demos <- readRDS(here::here("data", "all_demos_chi.Rda"))
all_demos.Chi <- readRDS(here::here("data", "all_demos_chi_proj.Rda"))
wards.2015 <- readRDS(here::here("data", "wards2015_sf.Rda"))
chiCentralLongitude <- median(wards.2015$long)
ggplot(st_intersection(all_demos.Chi, wards.2015) %>% filter(!is.na(predominant_race))) +
geom_sf(aes(fill=predominant_race, alpha=max_pct), lwd = 0) +
scale_fill_dt("diverging", reverse=TRUE) +
scale_alpha(range = c(0.35, 0.9), guide="none") +
geom_sf(data = wards.2015, color="black", fill=NA) +
geom_text_repel(data=wards.2015 %>% filter(ward %in% c(10, 18)),
aes(x=long, y=lat, label=ward),
fontface="bold",
force = 5,
size = 6,
direction = "both",
hjust=0) +
geom_text_repel(data=wards.2015 %>% filter(ward %in% c(9, 13, 14, 33, 34)),
aes(x=long, y=lat, label=ward),
nudge_x = -.35,
segment.size = 0.5,
segment.color = get_dt_cols("cocoa"),
fontface="bold",
size = 6,
force = 5,
direction = "both",
hjust=0) +
geom_text_repel(data = wards.2015 %>% filter(ward %in% c(11)),
aes(x=long, y=lat, label=ward),
nudge_x = .15,
segment.size = 0.5,
segment.color = get_dt_cols("cocoa"),
fontface="bold",
size = 6,
force = 10,
direction = "both",
hjust = 1) +
coord_sf(datum = NA) +
theme_map_modest() +
theme(plot.margin = unit(c(20, 0, 0, 0), "pt"),
legend.title = element_text(size=15),
legend.text = element_text(size=12),
plot.title = element_text(size = 20, face = "bold", hjust="0.5", margin = margin(t = 15)),
plot.subtitle = element_text(size = 15, margin = margin(t = 15)),
plot.caption = element_text(size = 15)) +
facet_wrap( ~ id) + labs(
title = "Latinx Populations in Chicago\nPushed to South, West Neighborhoods",
subtitle = "Chicago Racial and Ethnic Group Movement\nby Census Tract Since 2012 (5 year averages)",
caption = "Source: U.S. Census Bureau", fill = "Predominant Race in Tract")
turnoutSides <- readRDS(here::here("data", "sides_turnout.Rda"))
turnoutDiff <- readRDS(here::here("data", "wards_turnout.Rda"))
ggplot(turnoutSides, aes(x=as.factor(YEAR), y=TURNOUT, group=as.factor(SIDE))) +
geom_line(size=1.5, alpha=0.75, color="grey", show.legend = FALSE) +
geom_line(data = filter(turnoutSides, (DIFFERENCE > -0)||(is.na(DIFFERENCE))), aes(colour=SIDE), size=1.5, show.legend=FALSE) +
scale_color_dt("mixed", reverse = TRUE) +
geom_line(data = turnoutDiff, aes(group=as.factor(WARD), color=SIDE), size=0.5, alpha=0.25, show.legend = FALSE) +
geom_label(data = turnoutDiff %>% group_by(YEAR, SIDE) %>% summarise(MEAN_TURNOUT = round(mean(TURNOUT, na.rm=TRUE),1)),
aes(x=as.factor(YEAR), y= MEAN_TURNOUT, label = paste0(MEAN_TURNOUT, "%"), group=as.factor(SIDE)),
color = "darkgray",
label.padding = unit(0.05, "lines"),
label.size = 0.0,
fontface = "bold",
size = 4) +
geom_label_repel(data = turnoutSides %>% filter(YEAR == 2011),
aes(label = paste0(SIDE), color = SIDE),
fill=NA,
hjust = "left",
nudge_x = -.25,
force=5,
direction = "both",
fontface = "bold",
point.padding = 3,
size = 5,
show.legend = FALSE) +
geom_label_repel(data = turnoutSides %>% filter(YEAR == 2015),
aes(label = paste0(SIDE), color = SIDE),
fill=NA,
hjust = "right",
nudge_x = .25,
force = 7.5,
direction = "both",
fontface = "bold",
point.padding = 3,
size = 5,
show.legend = FALSE) +
scale_x_discrete(position = "top") +
# coord_cartesian(ylim=c(23.5, 60)) +
theme_modest() +
theme(axis.text.x.top = element_text(size=15, vjust = -8, face="bold"),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_blank(),
plot.caption = element_text(size = 12),
panel.grid.minor=element_blank(),
panel.grid.major=element_blank()) +
labs(title="Highest Voter Turnout In Wards Facing Deinvestment",
subtitle="Change in Voter Turnout Between 2011 and 2015 Chicago City Council Elections",
caption="Source: Chicago Board of Election Commissioners", color="Chicago Area")
bus_licenses %>% filter(!is.na(WARD), active==1) %>%
group_by(activity_month, activity_yr, WARD, SIDE) %>%
summarise(business_count = n()) %>% group_by(WARD) %>% arrange(desc(business_count)) %>%
ggplot(aes(x=reorder(WARD, business_count, FUN = median), y=business_count)) +
geom_boxplot(aes(group=as.factor(WARD), fill=as.factor(SIDE)), alpha=0.5) +
scale_fill_dt("mixed") +
geom_text(data = bus_licenses %>% filter(!is.na(WARD), active==1) %>%
group_by(activity_month, activity_yr, WARD, SIDE) %>%
summarise(business_count = n()) %>% group_by(WARD) %>% arrange(desc(business_count)) %>%
summarise(median_issuances = median(business_count)) %>% arrange(median_issuances),
aes(x=as.factor(WARD), y = median_issuances + 8, group=as.factor(WARD), label=as.factor(WARD)),
color="#6E2C49", fontface="bold") +
scale_y_continuous(limits = c(0, 750)) +
# scale_x_discrete(breaks=seq(0,50,5)) +
labs(x="Chicago Council Ward", y="Average Monthly Business Count", caption="Data Source: Chicago Open Data Portal", title="Least New & Surviving Businesses in South, West Wards for 15+ Years", subtitle="Calumet Heights, Westlawn Average less than 25 Business License Issuances per month ",fill="Chicago Area") +
theme_modest() +
theme(
plot.margin = unit(c(20, 0, 0, 0), "pt"),
panel.grid.major.x = element_blank(),
axis.ticks = element_blank(),
axis.title = element_text(size=12),
legend.title = element_text(size=15),
legend.text = element_text(size=12),
axis.text.x = element_blank(),
plot.title = element_text(size = 20, face = "bold", hjust="0.5", margin = margin(t = 15)),
plot.subtitle = element_text(size = 15, margin = margin(t = 15)),
plot.caption = element_text(size = 15)
)
wards.shp.2015 <- here::here("data","Geofiles - Chicago Zip Code and Neighborhood","Boundaries - Wards (2015-)","geo_export_0bb2e9fd-20ca-415b-a96a-7722d72c1b41.shp")
wards2015 <- shapefile(wards.shp.2015)
wards2015@data <- mutate(wards2015@data, id = rownames(wards2015@data))
wards2015.points <- fortify(wards2015, region="id")
wards2015.df <- merge(wards2015.points, wards2015@data, by="id")
license_types <- c("ISSUE" = "New Business Licenses",
"RENEW" = "Business License Renewals")
bl <- bus_licenses %>% filter(activity_yr >= 2009, active == 1, `APPLICATION TYPE`%in% c('ISSUE', 'RENEW')) %>%
distinct(`LICENSE ID`, activity_yr, `APPLICATION TYPE`, LONGITUDE, LATITUDE)
ggmap::ggmap(ggmap::get_googlemap(center = c(lon = -87.732125, lat = 41.83379),
zoom = 10, scale = 1,
maptype ='terrain',
color = 'color',
key = Sys.getenv("GOOGLEMAPS_KEY"))) +
geom_point(data=bl, aes(x=LONGITUDE, y=LATITUDE, color=as.factor(activity_yr)), alpha = 0.05, show.legend = FALSE, na.rm = TRUE) +
geom_path(data = wards2015.df, aes(long,lat,group=group), color="black") +
geom_polygon(data = wards2015.df, aes(long,lat,group=group), fill=NA) +
scale_color_dt("desert") +
labs(y="Latitude", x="Longitude", colour="Year", title="Money in the Middle",
subtitle="Virtually No New Business Entry, Renwal in Chicago Wards 9, 10, 18, or 41 since\n2009",
caption = "Data Source: City of Chicago Department of Business Affairs and Consumer Protection"
) +
theme_modest() +
facet_grid(. ~ `APPLICATION TYPE`, labeller=as_labeller(license_types)) +
theme(
panel.border = element_blank(),
axis.text.y = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
axis.title = element_blank(),
axis.text.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
plot.title = element_text(size = 20, face = "bold", hjust="0.5", margin = margin(t = 15)),
plot.subtitle = element_text(size = 15, hjust="0.5", margin = margin(t = 20)),
plot.caption = element_text(size = 15)
) +
scale_x_continuous(limits = c(wards2015.df %>% dplyr::select(long) %>% min() - 0.1, wards2015.df %>% dplyr::select(long) %>% max() + 0.05), expand = c(0, 0)) +
scale_y_continuous(limits = c(wards2015.df %>% dplyr::select(lat) %>% min() - 0.1, wards2015.df %>% dplyr::select(lat) %>% max()) + 0.05, expand = c(0, 0))
